| 书目名称 | Fault Detection and Diagnosis in Industrial Systems |
| 编辑 | Leo H. Chiang,Evan L. Russell,Richard D. Braatz |
| 视频video | |
| 概述 | Covers a variety of data-driven process monitoring techniques.Includes detailed applications in chemical plant simulation.Expanded text with more homework problems and graphically-illustrated examples |
| 丛书名称 | Advanced Textbooks in Control and Signal Processing |
| 图书封面 |  |
| 描述 | Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data..The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process monitoring techniques presented include: Data-driven methods - principal component analysis, Fisher discriminant analysis, partial least squares and canonical variate analysis; Analytical Methods - parameter estimation, observer-based methods and parity relations; Knowledge-based methods - causal analysis, expert systems and pattern recognition..The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process monitoring techniques to a non-trivial simul |
| 出版日期 | Textbook 2001 |
| 关键词 | chemometrics; classification; fault detection; fault diagnosis; multivariate statistics; neural network; n |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4471-0347-9 |
| isbn_softcover | 978-1-85233-327-0 |
| isbn_ebook | 978-1-4471-0347-9Series ISSN 1439-2232 Series E-ISSN 2510-3814 |
| issn_series | 1439-2232 |
| copyright | Springer-Verlag London 2001 |
发表于 2025-3-21 19:38:52
